The Review Opinion Diversification (Revopid-2017) shared task focuses on selecting top-k reviews from a set of reviews for a particular product based on a specific criteria. In this paper, we describe our approaches and results for modeling the ranking of reviews based on their usefulness score, this being the first of the three subtasks under this shared task. Instead of posing this as a regression problem, we modeled this as a classification task where we want to identify whether a review is useful or not. We employed a bi-directional LSTM to represent each review and is used with a softmax layer to predict the usefulness score. We chose the review with highest usefulness score, then find its cosine similarity score with rest of the reviews. This is done in order to ensure diversity in the selection of top-k reviews. On the top-5 list prediction, we finished 3rd while in top-10 list one, we are placed 2nd in the shared task. We have discussed the model and the results in detail in the paper.

The IJCNLP 2017 shared task on Customer Feedback Analysis focuses on classifying customer feedback into one of a predefined set of categories or classes. In this paper, we describe our approach to this problem and the results on four languages, i.e. English, French, Japanese and Spanish. Our system implemented a bidirectional LSTM (Graves and Schmidhuber, 2005) using pre-trained glove (Pennington et al., 2014) and fastText (Joulin et al., 2016) embeddings, and SVM (Cortes and Vapnik, 1995) with TF-IDF vectors for classifying the feedback data which is described in the later sections. We also tried different machine learning techniques and compared the results in this paper. Out of the 12 participating teams, our systems obtained 0.65, 0.86, 0.70 and 0.56 exact accuracy score in English, Spanish, French and Japanese respectively. We observed that our systems perform better than the baseline systems in three languages while we match the baseline accuracy for Japanese on our submitted systems. We noticed significant improvements in Japanese in later experiments, matching the highest performing system that was submitted in the shared task, which we will discuss in this paper.